fixed diffusion call
This commit is contained in:
Johannes Stelzer
parent
87aba9a8dc
commit
7c48f5e234
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@ -17,7 +17,7 @@ import torch
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import numpy as np
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import warnings
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from typing import Optional
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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from utils import interpolate_spherical
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from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel
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from diffusers.models.attention_processor import (
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@ -49,6 +49,8 @@ class DiffusersHolder():
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self.width_img = self.width_latent * self.pipe.vae_scale_factor
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self.height_img = self.height_latent * self.pipe.vae_scale_factor
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self.is_sdxl_turbo = False
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def init_types(self):
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assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found."
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assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found."
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@ -90,27 +92,23 @@ class DiffusersHolder():
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if len(self.negative_prompt) > 1:
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self.negative_prompt = [self.negative_prompt[0]]
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def get_text_embedding(self, prompt, do_classifier_free_guidance=True):
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if self.use_sd_xl:
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pr_encoder = self.pipe.encode_prompt
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else:
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pr_encoder = self.pipe._encode_prompt
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prompt_embeds = pr_encoder(
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def get_text_embedding(self, prompt):
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text_embeddings = self.pipe.encode_prompt(
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prompt=prompt,
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prompt_2=prompt,
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device=self.device,
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prompt_2=None,
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device=self.pipe._execution_device,
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num_images_per_prompt=1,
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do_classifier_free_guidance=do_classifier_free_guidance,
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do_classifier_free_guidance=self.pipe.do_classifier_free_guidance,
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negative_prompt=self.negative_prompt,
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negative_prompt_2=self.negative_prompt,
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negative_prompt_2=None,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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pooled_prompt_embeds=None,
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negative_pooled_prompt_embeds=None,
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lora_scale=None,
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clip_skip=False,
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clip_skip=self.pipe.clip_skip,
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)
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return prompt_embeds
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return text_embeddings
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def get_noise(self, seed=420):
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@ -128,15 +126,6 @@ class DiffusersHolder():
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return latents
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# H = self.height_latent
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# W = self.width_latent
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# C = self.pipe.unet.config.in_channels
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# generator = torch.Generator(device=self.device).manual_seed(int(seed))
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# latents = torch.randn((1, C, H, W), generator=generator, dtype=self.dtype, device=self.device)
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# if self.use_sd_xl:
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# latents = latents * self.pipe.scheduler.init_noise_sigma
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return latents
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@torch.no_grad()
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def latent2image(
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self,
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@ -168,41 +157,6 @@ class DiffusersHolder():
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return image
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# if output_type == "np":
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# return np.asarray(image)
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# else:
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# return image
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# # xxx
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# if self.use_sd_xl:
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# # make sure the VAE is in float32 mode, as it overflows in float16
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# self.pipe.vae.to(dtype=torch.float32)
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# use_torch_2_0_or_xformers = isinstance(
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# self.pipe.vae.decoder.mid_block.attentions[0].processor,
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# (
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# AttnProcessor2_0,
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# XFormersAttnProcessor,
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# LoRAXFormersAttnProcessor,
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# LoRAAttnProcessor2_0,
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# ),
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# )
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# # if xformers or torch_2_0 is used attention block does not need
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# # to be in float32 which can save lots of memory
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# if use_torch_2_0_or_xformers:
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# self.pipe.vae.post_quant_conv.to(latents.dtype)
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# self.pipe.vae.decoder.conv_in.to(latents.dtype)
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# self.pipe.vae.decoder.mid_block.to(latents.dtype)
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# else:
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# latents = latents.float()
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# image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
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# image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0])[0]
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# if output_type == "np":
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# return np.asarray(image)
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# else:
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# return image
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def prepare_mixing(self, mixing_coeffs, list_latents_mixing):
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if type(mixing_coeffs) == float:
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@ -438,7 +392,7 @@ class DiffusersHolder():
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@torch.no_grad()
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def run_diffusion_sd_xl(
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def run_diffusion_sd_xl_turbo(
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self,
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text_embeddings: list,
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latents_start: torch.FloatTensor,
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@ -457,14 +411,12 @@ class DiffusersHolder():
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negative_prompt_2 = None
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num_images_per_prompt = 1
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eta = 0.0
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generator = None
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latents = None
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prompt_embeds = None
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negative_prompt_embeds = None
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pooled_prompt_embeds = None
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negative_pooled_prompt_embeds = None
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ip_adapter_image = None
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output_type = "pil"
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return_dict = True
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cross_attention_kwargs = None
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guidance_rescale = 0.0
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@ -475,8 +427,6 @@ class DiffusersHolder():
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negative_crops_coords_top_left = (0, 0)
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negative_target_size = None
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clip_skip = None
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callback_on_step_end = None
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callback_on_step_end_tensor_inputs = ["latents"]
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# 0. Default height and width to unet
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@ -561,7 +511,6 @@ class DiffusersHolder():
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num_warmup_steps = max(len(timesteps) - self.num_inference_steps * self.pipe.scheduler.order, 0)
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# 9. Optionally get Guidance Scale Embedding
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timestep_cond = None
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if self.pipe.unet.config.time_cond_proj_dim is not None:
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@ -573,6 +522,201 @@ class DiffusersHolder():
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self.pipe._num_timesteps = len(timesteps)
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for i, t in enumerate(timesteps):
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# Set the right starting latents
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# Write latents out and skip
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if i < idx_start:
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list_latents_out.append(None)
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continue
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elif i == idx_start:
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latents = latents_start.clone()
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# Mix latents for crossfeeding
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if i > 0 and list_mixing_coeffs[i] > 0:
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latents_mixtarget = list_latents_mixing[i - 1].clone()
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latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if self.pipe.do_classifier_free_guidance else latents
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latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
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# predict the noise residual
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added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
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if ip_adapter_image is not None:
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added_cond_kwargs["image_embeds"] = image_embeds
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noise_pred = self.pipe.unet(
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latent_model_input,
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t,
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encoder_hidden_states=prompt_embeds,
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timestep_cond=timestep_cond,
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cross_attention_kwargs=self.pipe.cross_attention_kwargs,
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added_cond_kwargs=added_cond_kwargs,
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return_dict=False,
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)[0]
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# perform guidance
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if self.pipe.do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + self.pipe.guidance_scale * (noise_pred_text - noise_pred_uncond)
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if self.pipe.do_classifier_free_guidance and self.pipe.guidance_rescale > 0.0:
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# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
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noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.pipe.guidance_rescale)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
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# Append latents
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list_latents_out.append(latents.clone())
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if return_image:
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return self.latent2image(latents)
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else:
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return list_latents_out
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@torch.no_grad()
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def run_diffusion_sd_xl(
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self,
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text_embeddings: tuple,
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latents_start: torch.FloatTensor,
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idx_start: int = 0,
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list_latents_mixing=None,
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False,
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):
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prompt_2 = None
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height = None
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width = None
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timesteps = None
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denoising_end = None
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negative_prompt_2 = None
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num_images_per_prompt = 1
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eta = 0.0
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generator = None
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latents = None
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prompt_embeds = None
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negative_prompt_embeds = None
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pooled_prompt_embeds = None
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negative_pooled_prompt_embeds = None
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ip_adapter_image = None
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output_type = "pil"
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return_dict = True
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cross_attention_kwargs = None
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guidance_rescale = 0.0
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original_size = None
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crops_coords_top_left = (0, 0)
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target_size = None
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negative_original_size = None
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negative_crops_coords_top_left = (0, 0)
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negative_target_size = None
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clip_skip = None
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callback = None
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callback_on_step_end = None
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callback_on_step_end_tensor_inputs = ["latents"]
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# kwargs are additional keyword arguments and don't need a default value set here.
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# 0. Default height and width to unet
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height = height or self.pipe.default_sample_size * self.pipe.vae_scale_factor
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width = width or self.pipe.default_sample_size * self.pipe.vae_scale_factor
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original_size = original_size or (height, width)
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target_size = target_size or (height, width)
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# 1. Check inputs. skipped.
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self.pipe._guidance_scale = self.guidance_scale
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self.pipe._guidance_rescale = guidance_rescale
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self.pipe._clip_skip = clip_skip
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self.pipe._cross_attention_kwargs = cross_attention_kwargs
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self.pipe._denoising_end = denoising_end
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self.pipe._interrupt = False
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# 2. Define call parameters
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list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
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batch_size = 1
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device = self.pipe._execution_device
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# 3. Encode input prompt
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lora_scale = None
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(
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prompt_embeds,
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negative_prompt_embeds,
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pooled_prompt_embeds,
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negative_pooled_prompt_embeds,
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) = text_embeddings
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# 4. Prepare timesteps
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timesteps, num_inference_steps = retrieve_timesteps(self.pipe.scheduler, self.num_inference_steps, device, timesteps)
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# 5. Prepare latent variables
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num_channels_latents = self.pipe.unet.config.in_channels
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latents = latents_start.clone()
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list_latents_out = []
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# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
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extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta)
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# 7. Prepare added time ids & embeddings
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add_text_embeds = pooled_prompt_embeds
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if self.pipe.text_encoder_2 is None:
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text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
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else:
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text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim
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add_time_ids = self.pipe._get_add_time_ids(
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original_size,
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crops_coords_top_left,
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target_size,
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dtype=prompt_embeds.dtype,
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text_encoder_projection_dim=text_encoder_projection_dim,
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)
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if negative_original_size is not None and negative_target_size is not None:
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negative_add_time_ids = self.pipe._get_add_time_ids(
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negative_original_size,
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negative_crops_coords_top_left,
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negative_target_size,
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dtype=prompt_embeds.dtype,
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text_encoder_projection_dim=text_encoder_projection_dim,
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)
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else:
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negative_add_time_ids = add_time_ids
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if self.pipe.do_classifier_free_guidance:
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
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add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
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prompt_embeds = prompt_embeds.to(device)
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add_text_embeds = add_text_embeds.to(device)
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add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
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if ip_adapter_image is not None:
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output_hidden_state = False if isinstance(self.pipe.unet.encoder_hid_proj, ImageProjection) else True
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image_embeds, negative_image_embeds = self.pipe.encode_image(
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ip_adapter_image, device, num_images_per_prompt, output_hidden_state
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)
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if self.pipe.do_classifier_free_guidance:
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image_embeds = torch.cat([negative_image_embeds, image_embeds])
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image_embeds = image_embeds.to(device)
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# 8. Denoising loop
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num_warmup_steps = max(len(timesteps) - num_inference_steps * self.pipe.scheduler.order, 0)
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# 9. Optionally get Guidance Scale Embedding
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timestep_cond = None
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if self.pipe.unet.config.time_cond_proj_dim is not None:
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guidance_scale_tensor = torch.tensor(self.pipe.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
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timestep_cond = self.pipe.get_guidance_scale_embedding(
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guidance_scale_tensor, embedding_dim=self.pipe.unet.config.time_cond_proj_dim
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).to(device=device, dtype=latents.dtype)
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self.pipe._num_timesteps = len(timesteps)
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for i, t in enumerate(timesteps):
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# Set the right starting latents
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# Write latents out and skip
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@ -622,6 +766,8 @@ class DiffusersHolder():
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# Append latents
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list_latents_out.append(latents.clone())
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if return_image:
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return self.latent2image(latents)
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else:
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@ -632,72 +778,108 @@ class DiffusersHolder():
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#%%
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if __name__ == "__main__":
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from PIL import Image
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#%%
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from diffusers import AutoencoderTiny
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# pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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pipe.to('cuda') # xxx
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#%
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pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
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pipe.vae = pipe.vae.cuda()
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#%%
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# # xxx
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# self.set_dimensions((512, 512))
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# self.set_num_inference_steps(4)
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# self.guidance_scale = 2
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# # self.set_dimensions(1536, 1024)
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# latents_start = torch.randn((1,4,64//1,64)).half().cuda()
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# # latents_start = self.get_noise()
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# list_latents_1 = self.run_diffusion_sd_xl(text_embeddings, latents_start)
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# img_orig = self.latent2image(list_latents_1[-1])
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#%%
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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# pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
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pipe.to("cuda")
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#%
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# pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
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# pipe.vae = pipe.vae.cuda()
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#%% resanity
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import time
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self = DiffusersHolder(pipe)
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num_inference_steps = 4
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prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution"
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negative_prompt = "blurry, ugly, pale"
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num_inference_steps = 30
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guidance_scale = 4
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self.set_num_inference_steps(num_inference_steps)
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latents_start = self.get_noise()
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guidance_scale = 0
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self.guidance_scale = 0
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self.guidance_scale = guidance_scale
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#% get embeddings1
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prompt1 = "Photo of a colorful landscape with a blue sky with clouds"
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text_embeddings1 = self.get_text_embedding(prompt1)
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prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1
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prefix='full'
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for i in range(10):
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self.set_negative_prompt(negative_prompt)
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#% get embeddings2
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prompt2 = "Photo of a tree"
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text_embeddings2 = self.get_text_embedding(prompt2)
|
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prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2
|
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text_embeddings = self.get_text_embedding(prompt1)
|
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latents_start = self.get_noise(np.random.randint(111111))
|
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|
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latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False)
|
||||
t0 = time.time()
|
||||
|
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img1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
|
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img1B = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
|
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# img_refx = self.pipe(prompt=prompt1, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)[0]
|
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|
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img_refx = self.run_diffusion_sd_xl_resanity(text_embeddings=text_embeddings, latents_start=latents_start, return_image=True)
|
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|
||||
|
||||
# latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False)
|
||||
|
||||
|
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# # check if brings same image if restarted
|
||||
# img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True)
|
||||
|
||||
# mix latents
|
||||
#%%
|
||||
idx_mix = 2
|
||||
fract=0.8
|
||||
latents_start_mixed = interpolate_spherical(latents1[idx_mix-1], latents2[idx_mix-1], fract)
|
||||
prompt_embeds = interpolate_spherical(prompt_embeds1, prompt_embeds2, fract)
|
||||
pooled_prompt_embeds = interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, fract)
|
||||
negative_prompt_embeds = negative_prompt_embeds1
|
||||
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1
|
||||
text_embeddings_mix = [prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds]
|
||||
|
||||
self.run_diffusion_sd_xl(text_embeddings_mix, latents_start_mixed, idx_start=idx_start, return_image=True)
|
||||
dt_ref = time.time() - t0
|
||||
img_refx.save(f"x_{prefix}_{i}.jpg")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# xxx
|
||||
|
||||
# self.set_negative_prompt(negative_prompt)
|
||||
# self.set_num_inference_steps(num_inference_steps)
|
||||
# text_embeddings1 = self.get_text_embedding(prompt1)
|
||||
# prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1
|
||||
# latents_start = self.get_noise(420)
|
||||
# t0 = time.time()
|
||||
# img_dh = self.run_diffusion_sd_xl_resanity(text_embeddings1, latents_start, idx_start=0, return_image=True)
|
||||
# dt_dh = time.time() - t0
|
||||
|
||||
|
||||
"""
|
||||
sth bad in call
|
||||
sth bad in cond
|
||||
sth bad in noise
|
||||
"""
|
||||
|
||||
# xxxx
|
||||
# #%%
|
||||
|
||||
# self = DiffusersHolder(pipe)
|
||||
# num_inference_steps = 4
|
||||
# self.set_num_inference_steps(num_inference_steps)
|
||||
# latents_start = self.get_noise(420)
|
||||
# guidance_scale = 0
|
||||
# self.guidance_scale = 0
|
||||
|
||||
# #% get embeddings1
|
||||
# prompt1 = "Photo of a colorful landscape with a blue sky with clouds"
|
||||
# text_embeddings1 = self.get_text_embedding(prompt1)
|
||||
# prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1
|
||||
|
||||
# #% get embeddings2
|
||||
# prompt2 = "Photo of a tree"
|
||||
# text_embeddings2 = self.get_text_embedding(prompt2)
|
||||
# prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2
|
||||
|
||||
# latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False)
|
||||
|
||||
# img1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
|
||||
# img1B = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
|
||||
|
||||
|
||||
|
||||
# # latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False)
|
||||
|
||||
|
||||
# # # check if brings same image if restarted
|
||||
# # img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True)
|
||||
|
||||
# # mix latents
|
||||
# #%%
|
||||
# idx_mix = 2
|
||||
# fract=0.8
|
||||
# latents_start_mixed = interpolate_spherical(latents1[idx_mix-1], latents2[idx_mix-1], fract)
|
||||
# prompt_embeds = interpolate_spherical(prompt_embeds1, prompt_embeds2, fract)
|
||||
# pooled_prompt_embeds = interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, fract)
|
||||
# negative_prompt_embeds = negative_prompt_embeds1
|
||||
# negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1
|
||||
# text_embeddings_mix = [prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds]
|
||||
|
||||
# self.run_diffusion_sd_xl(text_embeddings_mix, latents_start_mixed, idx_start=idx_start, return_image=True)
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue